Delete loading script

phantom-wiki-v050.py

@@ -1,182 +0,0 @@
-"""Dataset script for PhantomWiki v0.5.
-
-Template: https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py
-"""
-
-
-import csv
-import json
-import os
-
-import datasets
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://github.com/albertgong1/phantom-wiki"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {}
-# Construct splits
-SIZES = [
-    25,
-    50,
-    100,
-    200,
-    300,
-    400,
-    500,
-    1000,
-    2500,
-    5000,
-    10000,
-]
-SPLITS = []
-for depth in [20]:
-    for size in SIZES:
-        for seed in [1, 2, 3]:
-            SPLITS.append(f"depth_{depth}_size_{size}_seed_{seed}")
-for filename, config in [("articles.json", "text-corpus"), ("questions.json", "question-answer"), ("facts.pl", "database")]:
-    _URLS[config] = {}
-    for split in SPLITS:
-        _URLS[config][split] = f"https://huggingface.co/datasets/mlcore/phantom-wiki-v050/resolve/main/{split}/{filename}"
-
-class PhantomWiki(datasets.GeneratorBasedBuilder):
-    """PhantomWiki v0.5"""
-
-    VERSION = datasets.Version("0.5.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="text-corpus", version=VERSION, description="This config contains the documents in the text corpus"),
-        datasets.BuilderConfig(name="question-answer", version=VERSION, description="This config containst the question-answer pairs"),
-        datasets.BuilderConfig(name="database", version=VERSION, description="This config contains the complete Prolog database"),
-    ]
-
-    # DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        """This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        """
-        if self.config.name == "text-corpus":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "title": datasets.Value("string"),
-                    "article": datasets.Value("string"),
-                    # "facts": datasets.Value("string"), # TODO
-                }
-            )
-        elif self.config.name == "question-answer":
-            # NOTE: to see available data types: https://huggingface.co/docs/datasets/v2.5.2/en/package_reference/main_classes#datasets.Features
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "intermediate_answers": datasets.Value("string"),
-                    "answer": datasets.Sequence(datasets.Value("string")),
-                    "prolog": datasets.Features(
-                        {
-                            "query": datasets.Value("string"),
-                            "answer": datasets.Value("string"),
-                        }
-                    ),
-                    "template": datasets.Sequence(datasets.Value("string")),
-                    "type": datasets.Value("int64"),  # this references the template type
-                    "difficulty": datasets.Value("int64"),
-                }
-            )
-        elif self.config.name == "database":
-            features = datasets.Features(
-                {
-                    "content": datasets.Value("string"),
-                }
-            )
-        else:
-            raise ValueError(f"Unknown configuration name {self.config.name}")
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        
-        NOTE: If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-        """
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
-        data_dir = dl_manager.download_and_extract(urls)
-        splits = []
-        for name, filepath in data_dir.items():
-            splits.append(datasets.SplitGenerator(
-                name=name,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": filepath,
-                    "split": name,
-                },
-            ))
-        return splits
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        if self.config.name in ["text-corpus", "question-answer"]:
-            with open(filepath, encoding="utf-8") as f:
-                for key, data in enumerate(json.load(f)):
-                    yield key, data
-        elif self.config.name == "database":
-            with open(filepath, encoding="utf-8") as f:
-                data = f.read()
-                # NOTE: Our schema expects a dictionary with a single key "content"
-                key = 0
-                yield key, {
-                    "content": data,
-                }
-        else:
-            raise ValueError(f"Unknown configuration name {self.config.name}")